Researchers are fascinated with predicting the stock market. Even though there is a large amount of supporting evidence that the dynamics of financial markets cannot be predicted, studies that employ creative prediction techniques continue to emerge. This study proposes a sentiment analysis model developed to infer the polarity of news articles related to a company. The process of collecting the dataset, as well as a diagram of the system architecture for the sentiment analysis engine used in this study is provided to readers. Insights from this research and experimental results are used to provide further proof that supports the Efficient Market Hypothesis.
Sorto, Max; Aasheim, Cheryl; and Wimmer, Hayden, "Feeling The Stock Market: A Study in the Prediction of Financial Markets Based on News Sentiment" (2017). SAIS 2017 Proceedings. 30.